Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults

This cohort study investigates the association of sociodemographic and lifestyle factors with biological age as measured by epigenetic clocks among younger adults.


Introduction
Research on aging has long documented social and demographic differentials in morbidity and mortality risk but has only recently been able to explore underlying molecular and cellular changes that accompany aging processes and shorten life.5][6][7][8] In particular, epigenomic profiling has greatly increased the availability of novel indicators of biological aging in the form of epigenetic clocks, [9][10][11] composite measures of DNA methylation (DNAm) that represent molecular evidence of disease risk and aging processes. 12,13igenetic clocks estimate epigenetic age, and the relative comparison of epigenetic age with chronological age represents epigenetic age acceleration (hereafter, biological aging), providing a measurement of differences in biological aging among individuals of the same calendar age. 14[17][18][19][20][21][22][23][24][25][26] Thus, they potentially represent useful measures for interventions intended to reduce social inequalities in healthy aging and longevity, 26 particularly if the clocks can detect biological aging in young individuals without apparent disease.
Considerable progress has been made in measuring biological age using numerous sources of data to create epigenetic clocks. 7,27,280][31] However, attention soon shifted to estimate aging outcomes beyond chronological age.Thus, second-generation epigenetic clocks were calibrated on differences in biological aging reflected by disease and mortality risks. 20,32Recent advances exploit longitudinal measurement of physical and cognitive function and disease risk over time to construct clocks that capture the pace of change in biological aging over time, 33,34 representing a third generation of epigenetic clocks.
[37][38][39] To the extent that certain social and lifestyle factors are known to be associated with increased age-related health risks, we would expect these factors to be associated with more rapid biological aging, affirming the research value of epigenetic clocks as markers of aging, particularly in younger adults.Indeed, lower socioeconomic status in education, income, and wealth has been associated with more rapid epigenetic aging. 8,40Variability in biological aging by sex generally shows that males experience more rapid epigenetic aging, 28,39,41 whereas variability by race and ethnicity is inconsistent, with positive, negative, and null differences observed in previous research comparing Black or Hispanic with White individuals. 5,99]28 Overall, results suggest that more recent, second-and third-generation clocks are more sensitive to social and environmental exposures, although more work is needed to better understand whether and how clocks capture shared or distinct aspects of aging.
Existing research examining sociodemographic factors and biological aging has notable gaps.
First, many studies use a single or a few clocks, making it difficult to ascertain whether results are

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Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults consistent across clocks.Second, most epigenetic research relies on small, often local, and nondiverse samples, limiting generalizability. 7,13,14,40Some recent studies have used large representative samples that examined a range of clocks but focused on older adults. 10,41In fact, most research on epigenetic aging has been based on samples of older adults 10,14,15,20,[22][23][24]38,[42][43][44][45] (exceptions include studies by Aanes et al 46 and Raffington et al 47 ). Given tholder adults are more likely to have chronic comorbidities, it is difficult to disentangle outcomes associated with underlying disease from those associated with sociodemographic exposures. Furthrmore, as age increases, biological age may become a less reliable estimator of health outcomes due to mortality selection and increased biological heterogeneity in older age.

Methods
Data in this cohort study come from Add Health, a nationally representative cohort study of US adolescents in grades 7 to 12 in 1994 who were followed up for 25 years across 5 interview waves. 44 use data from wave I (WI; 1994-1995) and wave V (WV; 2016-18), when the cohort was aged 33 to 44 years.During the WV survey, 5381 of 12 300 participants consented to and completed a follow-up in-person home exam, when venous blood was drawn (93.1% consent rate) for DNAm assay.After removal of samples that did not pass quality control and elimination of replicates, the DNAm sample included 4582 participants.The sample size was further reduced to 4237 participants due to missing values on sociodemographic factors.Population representation was maintained across waves and samples (eTable 1 in Supplement 1).
Methylation analysis was conducted using the Illumina Infinium chemistry. 48DNAm levels across approximately 850 000 CpG sites were measured using the Infinium Methylation EPIC BeadChip (Illumina, Inc).We measured β values for CpG sites across the genome according to kit protocols and filtered to remove polymorphic positions.Remaining CpG sites were restricted to a set of 30 484 CpG sites used with DNA methylation calculator 49 and Methylcipher 50 and Dunedin 33,34 calculators; principal component (PC) clocks were based on 78 464 CpG sites and used code publicly available on GitHub (eAppendix 1 in Supplement 1).
Adolescent participants and their parents or caregivers provided written consent at WI; young adult participants provided consent at WV for the survey administration and epigenetic data collection.Consent obtained at WV extends to the current study.This study was approved by the institutional review board of the University of North Carolina at Chapel Hill.We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Epigenetic Clocks
We constructed 16 epigenetic clocks when the mean (SD) cohort age was 38.4 (2.0) years at the time of venous blood draw (Table 1; eAppendix 2 in Supplement 1).Clocks are shown in order of their generation, beginning with 5 first-generation clocks: Horvath1, Horvath2, Hannum, VidalBralo, and Zhang2019.The 2 Horvath clocks were constructed across multiple tissues as a pantissue clock of chronological age and differ by the number of CpG sites on which the algorithm was based. 30,31The Hannum 29 and VidalBralo 45  PhenoAge, 20 and GrimAge, 21 which also incorporated smoking-associated methylation changes.
To reduce technical variation in CpG β values on which epigenetic clocks were based, Higgins-

Sociodemographic and Lifestyle Characteristics
Age was a continuous measure of chronological age at WV blood draw.Participants reported sex assigned at birth at WI (female or male).We assessed differences in epigenetic aging by race and ethnicity because prior evidence reported mixed results.

Statistical Analysis
We conducted descriptive statistical analysis on the 16 clocks and sample characteristics and examined correlations among epigenetic clocks, chronological age, and measures of accelerated and pace of aging.We performed weighted linear regression of accelerated biological aging on sociodemographic and lifestyle factors in bivariate and multivariable models adjusted for chronological age 54 with and without controls for cell composition (see eMethods in Supplement 1 for model assumptions and eTable 2 in Supplement 1 for distributional attributes of clock measures).
We used sampling weights in all analyses to produce national estimates 44,55,56 and the R statistical library Survey.

Results
We   Weighted bivariate associations between sociodemographic characteristics and epigenetic age acceleration for selected first-, second-, and third-generation clocks are presented in a forest plot.Error bars indicate 95% CIs.Age acceleration units are expressed in years for Horvath1AA, PhenoAgeAA, and GrimAgeAA, while DunedinPACE is measured as a rate of biological aging in SD.Horvath1AA is a first-generation clock, while PhenoAgeAA and GrimAgeAA are second-generation clocks and DunedinPACE is a third-generation clock.The other category for race or ethnicity includes participants who identified as American Indian or Alaska Native or who checked some other race or origin.Alcohol use categories were none, light drinking (<daily and no binge drinking), and heavy (daily drinking) or binge drinking.

Discussion
Based on the current literature, this cohort study estimated 16 epigenetic clocks primarily developed for older cohorts to assess their distribution, correlations with chronological age and with each other, and variability across sociodemographic and lifestyle characteristics known to estimate morbidity and mortality in prior research using the younger adult Add Health cohort.While it makes sense that most epigenetic research focuses on older adults, there is increasing recognition that molecular processes underlying disease risk begin long before overt disease is evident in chronologically older adults. 59,60 found that clock measures displayed a range of estimated epigenetic ages for younger adults that had moderate Pearson r values for correlation with chronological age and with each other.
This result is consistent with prior epigenetic clock research on older populations. 9,11These wideranging results suggest that different clocks may reflect distinct aspects of aging given that they are based on the assessment of methylation at highly disparate numbers of CpG sites, trained on different populations that vary by age and race and ethnicity, and developed in different tissues. 5vertheless, many social and lifestyle factors were associated with biological aging as shown by second-and third-generation clocks in the expected direction according to prior research on inequalities in health and mortality risks 26,61,62 even in this sample of adults about midlife.In particular, GrimAge, PCGrimAge, and DunedinPACE showed accelerated aging among individuals with no or some college education compared with those with a college degree and for those at near or below poverty-level incomes compared with those with incomes greater than $100 000.
Importantly and consistent with other research, severe obesity and lack of weekly exercise were also associated with faster biological aging in second-and third-generation clocks.Given that secondand third-generation clocks were trained to estimate disease and mortality risks while firstgeneration clocks were trained on chronological age, our findings support conclusions that the biological aging process may be underway prior to midlife and later life and that these second-and third-generation clocks are sensitive measures of this process before age-related disease comorbidities are present.Thus, our findings suggest that epigenetic clocks may represent surrogate end points in interventions designed to address social determinants of healthy aging.
We found interesting new results for immigrant status.Despite the often stressful and discriminatory contexts in which immigrants live, those with US-born status even if they had foreignborn parents in the second generation experienced faster aging, suggesting that the immigrant advantage found in much prior research remains biologically embedded. 63,64However, this result may be driven by the large and heterogenous Hispanic population that comprised immigrant groups given that Hispanic individuals tend to have lower biological aging for second-and thirdgeneration clocks.

Limitations
This study has several limitations.One potential limitation of our research is the small age range of adults in Add Health.This limitation could be related to inconsistent results we found for race and ethnicity and sex, although these findings were also consistent with prior research. 5,9,11,35,65,66There are differing views on whether epigenetic markers can be analyzed in pooled racial and ethnic samples, especially because many epigenetic clocks have been developed in predominantly White samples. 7,13,20,67It remains to be investigated whether there are varying responses to social and environmental exposures in different racial and ethnic groups. 67Prior research identified changes in biological aging by sex that is chronological age related (eg, females experience more rapid aging during menopause 38 ), which should not affect our young adult sample.However, females also have variability in immunity over time, especially during and after pregnancy, and experience variation in autoimmunity and hormones. 68This variation could be related to some outcomes on which clocks are or are not trained.In addition, other social factors we did not include may explain some of the mixed sex and race and ethnicity findings.
Our research addresses existing gaps by investigating the association of sociodemographic and lifestyle factors with biological age according to 16 DNAm measures in a diverse population of adults aged 33 to 44 years from the US representative National Longitudinal Study of Adolescent to Adult Health (Add Health).Using new methylation data with national representation of racial and ethnic, socioeconomic, and geographic groups, we contribute to the limited research on epigenetic aging in younger adults.To our knowledge, our study is one of few to examine the emergence of sociodemographic inequalities in aging before adults enter midlife across established epigenetic clock measures.

Figure 2 .
Figure 2. Bivariate Associations Between Sociodemographics and Epigenetic Age in Selected Clocks

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clocks were trained on blood samples, and Zhang2019 51 was trained on blood and saliva samples to estimate chronological age.We examined 3 second-

Table 1 .
53en et al52retrained Hannum, Horvath1, Horvath2, GrimAge, and PhenoAge clocks on PCs of CpG methylation values rather than individual CpGs, with the goal of reducing the effects of technical noise at any given individual CpG.We refer to these as PC clocks (Table1).For each of these 13 firstand second-generation clocks, we calculated biological age by taking residuals of the clock values regressed on chronological age.We refer to the final set of 3 clocks as third generation, which use a different unit of measurement: DunedinPoAm, Dunedin PACE, and Zhang2017.DunedinPoAm33and Dunedin PACE34estimate the pace of biological aging in SD units based on changes in biomarkers of organ system dysfunction, and Zhang2017 estimates a continuous risk score of all-cause mortality.53DescriptiveStatistics for Epigenetic Clocks and Pearson Correlation With Age (N = 4237) a Zhang2017-measured risk of mortality, RR −1.3 (0.4) [−2.6 to 0.4] 0.17 <0.01 Abbreviations: PC, principal component; RR risk ratio.a Chronological age was assessed at the wave V blood draw.Clock measures are in units of years (positive) or a slower (negative) rate of epigenetic aging (ie, biological aging) relative to the rate of chronological age over time.Risk of mortality is in units of mortality RR, with negative values indicating a lower risk and positive values indicating a higher risk.
5,9Race or ethnicity were self-identified at WV based on 1 question that asked participants, "What is your race or ethnic origin?"Response categories included American Indian or Alaska Native, Asian, Black or African American, Hispanic, Pacific Islander, White, and some other race or origin.For participants with missing data at WV, we used WI self-reports of race and ethnicity.Small sample sizes required us to combine Pacific Islander with the Asian category and American Indian or Alaska Native with the other category, although we show the full distribution in eTable 1 in Supplement 1.We measured immigrant generation at WI (first, current), and alcohol use (none, light [<daily and no binge drinking], and heavy [daily] or binge).See eAppendix 3 in Supplement 1 for details on variable construction.

Table 2 .
Participant CharacteristicsCorrelations between epigenetic clocks and epigenetic age acceleration measures are depicted as a heat map.A, Pearson correlations among all epigenetic clocks for chronological age are presented.B, Pearson correlations among all epigenetic age acceleration measures, including third-generation rates of aging, are presented.Darker hues indicated a higher Pearson r correlation value.
Figure 1.Correlations Between Epigenetic Clocks and Epigenetic Age Acceleration

Table 3 .
Multivariate Models of Social and Demographic Characteristics and Selected Clock Estimates (N = 4237) The other category for race or ethnicity includes participants who identified as American Indian or Alaska Native or who checked some other race or origin.
a b Body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) ranges for the obesity categories are: reference range or underweight, BMI<25; overweight, 25 Յ BMI < 30; obesity, 30 Յ BMI < 40; severe obesity, BMI Ն 40.cAlcohol use is categorized as: "none" if participants indicated they never drank alcohol; "light" if participants reported drinking alcohol less than daily and did not binge drink; and "heavy or binge" if participants reported engaging in binge drinking in the last year or drinking daily in the last month or last year. 67

JAMA Network Open | Genetics and Genomics SUPPLEMENT 1. eAppendix 1.
Data Quality and Curation of Epigenetic Data in Add Health eAppendix 2. Description of Epigenetic Clock Measures eAppendix 3. Sociodemographic and Lifestyle Measures eMethods.eFigure.Bivariate Associations Between Sociodemographic Factors and Epigenetic Age According to Clocks eTable 1. Add Health Sample Distribution of Demographic Characteristics for Wave I, Wave V Full Sample, and Wave V Epigenetic Sample eTable 2. Univariate Statistics for DNA Methylation Epigenetic Clocks (N = 4237) Bivariate Models of Social and Demographic Characteristics and Clock Estimates (N = 4237) eTable 4. Multivariate Models of Social and Demographic Characteristics and Clock Estimates (N = 4237) eTable 5. Multivariate Models of Social and Demographic Characteristics and Clock Estimates With Cell Compositions (N = 4237)